The present invention generally relates to scheduling users in a wireless communication network, and particularly relates to scheduling such users according to one or more Quality of Service (QoS) guarantees.
Many aspects of operation in the typical wireless communication network reflect the natural tension between serving as many users as possible, i.e., maximizing revenue, while still providing those users with acceptable quality of service and maintaining the network within its capacity limits. For example, several of the evolving network standards feature one or more relatively high-speed data channels that are time-shared between a potentially large pool of users. High-speed packet data channels such as those defined in 1xEV-DO and 1xEV-DV variants of cdma2000, as well as the High Speed Downlink Packet Access (HSDPA) channel defined in Wideband CDMA (WCDMA), stand as examples of such time-shared, scheduled-use channels.
Traditionally, such scheduling focuses either on maximizing the aggregate data rate of shared service, or on ensuring some degree of service “fairness” between users sharing the packet data channel. The former approach is often referred to as “Maximum Carrier-to-Interference” scheduling (Max C/I scheduling) because the users having the best channel conditions, i.e., the best carrier-to-interference ratio, are preferentially scheduled since those users can be served at higher data rates than users with less favorable channel conditions. The latter approach often is referred to as “Proportional Fair” scheduling because users are preferentially serviced based on their past average rates of service relative to their requested rates. That is, a historically underserved user moves higher in preference as that user's average data rate falls increasingly short of that user's requested data rate.
Some approaches to user scheduling combine aspects of proportional fair and Max C/I scheduling, while other approaches eschew the “steepest gradient,” derivative-based analyses of both the Max C/I and proportional fair scheduling techniques, looking instead at the aggregate benefit associated with serving particular ones of the users. Regardless, one longstanding shortcoming of existent approaches to user scheduling is the failure to observe or otherwise comply with QoS standards applicable to individual users, or to classes of users.
Such a shortcoming is exacerbated by the increasing emphasis on QoS as network service providers and users explore various approaches to a wide range of data services, with each service type having varying and, oftentimes, distinct levels (and types) of QoS constraints for acceptable performance. That is, some types of services may require minimum values for one or more QoS parameters. For example, video streaming applications demand well-constrained overall packet delivery latencies as well as minimal packet jitter. Moreover, at least some users in general are likely willing to pay for higher QoS guarantees. However, entering into service contracts with such QoS guarantees obligates service providers to work toward the guaranteed levels of service, which requires that user-scheduling decisions incorporate consideration of such QoS guarantees.